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/******************************************************************************
* Copyright (c) 2011, Duane Merrill. All rights reserved.
* Copyright (c) 2011-2018, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions are met:
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in the
* documentation and/or other materials provided with the distribution.
* * Neither the name of the NVIDIA CORPORATION nor the
* names of its contributors may be used to endorse or promote products
* derived from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
* ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
* WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
* DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
* DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
* (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
* ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
* SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*
******************************************************************************/
/******************************************************************************
* Test of DeviceReduce utilities
******************************************************************************/
// Ensure printing of CUDA runtime errors to console
#define CUB_STDERR
#include <stdio.h>
#include <limits>
#include <typeinfo>
#include <thrust/device_ptr.h>
#include <thrust/reduce.h>
#include <cub/util_allocator.cuh>
#include <cub/device/device_reduce.cuh>
#include <cub/device/device_segmented_reduce.cuh>
#include <cub/iterator/constant_input_iterator.cuh>
#include <cub/iterator/discard_output_iterator.cuh>
#include <cub/iterator/transform_input_iterator.cuh>
#include "test_util.h"
using namespace cub;
//---------------------------------------------------------------------
// Globals, constants and typedefs
//---------------------------------------------------------------------
int g_ptx_version;
int g_sm_count;
bool g_verbose = false;
bool g_verbose_input = false;
int g_timing_iterations = 0;
int g_repeat = 0;
CachingDeviceAllocator g_allocator(true);
// Dispatch types
enum Backend
{
CUB, // CUB method
CUB_SEGMENTED, // CUB segmented method
CUB_CDP, // GPU-based (dynamic parallelism) dispatch to CUB method
THRUST, // Thrust method
};
// Custom max functor
struct CustomMax
{
/// Boolean max operator, returns <tt>(a > b) ? a : b</tt>
template <typename OutputT>
__host__ __device__ __forceinline__ OutputT operator()(const OutputT &a, const OutputT &b)
{
return CUB_MAX(a, b);
}
};
//---------------------------------------------------------------------
// Dispatch to different CUB DeviceReduce entrypoints
//---------------------------------------------------------------------
/**
* Dispatch to reduce entrypoint (custom-max)
*/
template <typename InputIteratorT, typename OutputIteratorT, typename OffsetIteratorT, typename ReductionOpT>
CUB_RUNTIME_FUNCTION __forceinline__
cudaError_t Dispatch(
Int2Type<CUB> dispatch_to,
int timing_timing_iterations,
size_t *d_temp_storage_bytes,
cudaError_t *d_cdp_error,
void* d_temp_storage,
size_t& temp_storage_bytes,
InputIteratorT d_in,
OutputIteratorT d_out,
int num_items,
int max_segments,
OffsetIteratorT d_segment_offsets,
ReductionOpT reduction_op,
cudaStream_t stream,
bool debug_synchronous)
{
typedef typename std::iterator_traits<InputIteratorT>::value_type InputT;
// The output value type
typedef typename If<(Equals<typename std::iterator_traits<OutputIteratorT>::value_type, void>::VALUE), // OutputT = (if output iterator's value type is void) ?
typename std::iterator_traits<InputIteratorT>::value_type, // ... then the input iterator's value type,
typename std::iterator_traits<OutputIteratorT>::value_type>::Type OutputT; // ... else the output iterator's value type
// Max-identity
OutputT identity = Traits<InputT>::Lowest(); // replace with std::numeric_limits<OutputT>::lowest() when C++ support is more prevalent
// Invoke kernel to device reduction directly
cudaError_t error = cudaSuccess;
for (int i = 0; i < timing_timing_iterations; ++i)
{
error = DeviceReduce::Reduce(d_temp_storage, temp_storage_bytes,
d_in, d_out, num_items, reduction_op, identity,
stream, debug_synchronous);
}
printf("\t timing_timing_iterations: %d, temp_storage_bytes: %lld
",
timing_timing_iterations, temp_storage_bytes);
return error;
}
/**
* Dispatch to sum entrypoint
*/
template <typename InputIteratorT, typename OutputIteratorT, typename OffsetIteratorT>
CUB_RUNTIME_FUNCTION __forceinline__
cudaError_t Dispatch(
Int2Type<CUB> dispatch_to,
int timing_timing_iterations,
size_t *d_temp_storage_bytes,
cudaError_t *d_cdp_error,
void* d_temp_storage,
size_t& temp_storage_bytes,
InputIteratorT d_in,
OutputIteratorT d_out,
int num_items,
int max_segments,
OffsetIteratorT d_segment_offsets,
cub::Sum reduction_op,
cudaStream_t stream,
bool debug_synchronous)
{
// Invoke kernel to device reduction directly
cudaError_t error = cudaSuccess;
for (int i = 0; i < timing_timing_iterations; ++i)
{
error = DeviceReduce::Sum(d_temp_storage, temp_storage_bytes, d_in, d_out, num_items, stream, debug_synchronous);
}
printf("\t timing_timing_iterations: %d, temp_storage_bytes: %lld
",
timing_timing_iterations, temp_storage_bytes);
return error;
}
/**
* Dispatch to min entrypoint
*/
template <typename InputIteratorT, typename OutputIteratorT, typename OffsetIteratorT>
CUB_RUNTIME_FUNCTION __forceinline__
cudaError_t Dispatch(
Int2Type<CUB> dispatch_to,
int timing_timing_iterations,
size_t *d_temp_storage_bytes,
cudaError_t *d_cdp_error,
void* d_temp_storage,
size_t& temp_storage_bytes,
InputIteratorT d_in,
OutputIteratorT d_out,
int num_items,
int max_segments,
OffsetIteratorT d_segment_offsets,
cub::Min reduction_op,
cudaStream_t stream,
bool debug_synchronous)
{
// Invoke kernel to device reduction directly
cudaError_t error = cudaSuccess;
for (int i = 0; i < timing_timing_iterations; ++i)
{
error = DeviceReduce::Min(d_temp_storage, temp_storage_bytes, d_in, d_out, num_items, stream, debug_synchronous);
}
printf("\t timing_timing_iterations: %d, temp_storage_bytes: %lld
",
timing_timing_iterations, temp_storage_bytes);
return error;
}
/**
* Dispatch to max entrypoint
*/
template <typename InputIteratorT, typename OutputIteratorT, typename OffsetIteratorT>
CUB_RUNTIME_FUNCTION __forceinline__
cudaError_t Dispatch(
Int2Type<CUB> dispatch_to,
int timing_timing_iterations,
size_t *d_temp_storage_bytes,
cudaError_t *d_cdp_error,
void* d_temp_storage,
size_t& temp_storage_bytes,
InputIteratorT d_in,
OutputIteratorT d_out,
int num_items,
int max_segments,
OffsetIteratorT d_segment_offsets,
cub::Max reduction_op,
cudaStream_t stream,
bool debug_synchronous)
{
// Invoke kernel to device reduction directly
cudaError_t error = cudaSuccess;
for (int i = 0; i < timing_timing_iterations; ++i)
{
error = DeviceReduce::Max(d_temp_storage, temp_storage_bytes, d_in, d_out, num_items, stream, debug_synchronous);
}
printf("\t timing_timing_iterations: %d, temp_storage_bytes: %lld
",
timing_timing_iterations, temp_storage_bytes);
return error;
}
/**
* Dispatch to argmin entrypoint
*/
template <typename InputIteratorT, typename OutputIteratorT, typename OffsetIteratorT>
CUB_RUNTIME_FUNCTION __forceinline__
cudaError_t Dispatch(
Int2Type<CUB> dispatch_to,
int timing_timing_iterations,
size_t *d_temp_storage_bytes,
cudaError_t *d_cdp_error,
void* d_temp_storage,
size_t& temp_storage_bytes,
InputIteratorT d_in,
OutputIteratorT d_out,
int num_items,
int max_segments,
OffsetIteratorT d_segment_offsets,
cub::ArgMin reduction_op,
cudaStream_t stream,
bool debug_synchronous)
{
// Invoke kernel to device reduction directly
cudaError_t error = cudaSuccess;
for (int i = 0; i < timing_timing_iterations; ++i)
{
error = DeviceReduce::ArgMin(d_temp_storage, temp_storage_bytes, d_in, d_out, num_items, stream, debug_synchronous);
}
printf("\t timing_timing_iterations: %d, temp_storage_bytes: %lld
",
timing_timing_iterations, temp_storage_bytes);
return error;
}
/**
* Dispatch to argmax entrypoint
*/
template <typename InputIteratorT, typename OutputIteratorT, typename OffsetIteratorT>
CUB_RUNTIME_FUNCTION __forceinline__
cudaError_t Dispatch(
Int2Type<CUB> dispatch_to,
int timing_timing_iterations,
size_t *d_temp_storage_bytes,
cudaError_t *d_cdp_error,
void* d_temp_storage,
size_t& temp_storage_bytes,
InputIteratorT d_in,
OutputIteratorT d_out,
int num_items,
int max_segments,
OffsetIteratorT d_segment_offsets,
cub::ArgMax reduction_op,
cudaStream_t stream,
bool debug_synchronous)
{
// Invoke kernel to device reduction directly
cudaError_t error = cudaSuccess;
for (int i = 0; i < timing_timing_iterations; ++i)
{
error = DeviceReduce::ArgMax(d_temp_storage, temp_storage_bytes, d_in, d_out, num_items, stream, debug_synchronous);
}
printf("\t timing_timing_iterations: %d, temp_storage_bytes: %lld
",
timing_timing_iterations, temp_storage_bytes);
return error;
}
//---------------------------------------------------------------------
// Dispatch to different CUB DeviceSegmentedReduce entrypoints
//---------------------------------------------------------------------
/**
* Dispatch to reduce entrypoint (custom-max)
*/
template <typename InputIteratorT, typename OutputIteratorT, typename OffsetIteratorT, typename ReductionOpT>
CUB_RUNTIME_FUNCTION __forceinline__
cudaError_t Dispatch(
Int2Type<CUB_SEGMENTED> dispatch_to,
int timing_timing_iterations,
size_t *d_temp_storage_bytes,
cudaError_t *d_cdp_error,
void* d_temp_storage,
size_t& temp_storage_bytes,
InputIteratorT d_in,
OutputIteratorT d_out,
int num_items,
int max_segments,
OffsetIteratorT d_segment_offsets,
ReductionOpT reduction_op,
cudaStream_t stream,
bool debug_synchronous)
{
// The input value type
typedef typename std::iterator_traits<InputIteratorT>::value_type InputT;
// The output value type
typedef typename If<(Equals<typename std::iterator_traits<OutputIteratorT>::value_type, void>::VALUE), // OutputT = (if output iterator's value type is void) ?
typename std::iterator_traits<InputIteratorT>::value_type, // ... then the input iterator's value type,
typename std::iterator_traits<OutputIteratorT>::value_type>::Type OutputT; // ... else the output iterator's value type
// Max-identity
OutputT identity = Traits<InputT>::Lowest(); // replace with std::numeric_limits<OutputT>::lowest() when C++ support is more prevalent
// Invoke kernel to device reduction directly
cudaError_t error = cudaSuccess;
for (int i = 0; i < timing_timing_iterations; ++i)
{
error = DeviceSegmentedReduce::Reduce(d_temp_storage, temp_storage_bytes,
d_in, d_out, max_segments, d_segment_offsets, d_segment_offsets + 1, reduction_op, identity,
stream, debug_synchronous);
}
return error;
}
/**
* Dispatch to sum entrypoint
*/
template <typename InputIteratorT, typename OutputIteratorT, typename OffsetIteratorT>
CUB_RUNTIME_FUNCTION __forceinline__
cudaError_t Dispatch(
Int2Type<CUB_SEGMENTED> dispatch_to,
int timing_timing_iterations,
size_t *d_temp_storage_bytes,
cudaError_t *d_cdp_error,
void* d_temp_storage,
size_t& temp_storage_bytes,
InputIteratorT d_in,
OutputIteratorT d_out,
int num_items,
int max_segments,
OffsetIteratorT d_segment_offsets,
cub::Sum reduction_op,
cudaStream_t stream,
bool debug_synchronous)
{
// Invoke kernel to device reduction directly
cudaError_t error = cudaSuccess;
for (int i = 0; i < timing_timing_iterations; ++i)
{
error = DeviceSegmentedReduce::Sum(d_temp_storage, temp_storage_bytes,
d_in, d_out, max_segments, d_segment_offsets, d_segment_offsets + 1,
stream, debug_synchronous);
}
return error;
}
/**
* Dispatch to min entrypoint
*/
template <typename InputIteratorT, typename OutputIteratorT, typename OffsetIteratorT>
CUB_RUNTIME_FUNCTION __forceinline__
cudaError_t Dispatch(
Int2Type<CUB_SEGMENTED> dispatch_to,
int timing_timing_iterations,
size_t *d_temp_storage_bytes,
cudaError_t *d_cdp_error,
void* d_temp_storage,
size_t& temp_storage_bytes,
InputIteratorT d_in,
OutputIteratorT d_out,
int num_items,
int max_segments,
OffsetIteratorT d_segment_offsets,
cub::Min reduction_op,
cudaStream_t stream,
bool debug_synchronous)
{
// Invoke kernel to device reduction directly
cudaError_t error = cudaSuccess;
for (int i = 0; i < timing_timing_iterations; ++i)
{
error = DeviceSegmentedReduce::Min(d_temp_storage, temp_storage_bytes,
d_in, d_out, max_segments, d_segment_offsets, d_segment_offsets + 1,
stream, debug_synchronous);
}
return error;
}
/**
* Dispatch to max entrypoint
*/
template <typename InputIteratorT, typename OutputIteratorT, typename OffsetIteratorT>
CUB_RUNTIME_FUNCTION __forceinline__
cudaError_t Dispatch(
Int2Type<CUB_SEGMENTED> dispatch_to,
int timing_timing_iterations,
size_t *d_temp_storage_bytes,
cudaError_t *d_cdp_error,
void* d_temp_storage,
size_t& temp_storage_bytes,
InputIteratorT d_in,
OutputIteratorT d_out,
int num_items,
int max_segments,
OffsetIteratorT d_segment_offsets,
cub::Max reduction_op,
cudaStream_t stream,
bool debug_synchronous)
{
// Invoke kernel to device reduction directly
cudaError_t error = cudaSuccess;
for (int i = 0; i < timing_timing_iterations; ++i)
{
error = DeviceSegmentedReduce::Max(d_temp_storage, temp_storage_bytes,
d_in, d_out, max_segments, d_segment_offsets, d_segment_offsets + 1,
stream, debug_synchronous);
}
return error;
}
/**
* Dispatch to argmin entrypoint
*/
template <typename InputIteratorT, typename OutputIteratorT, typename OffsetIteratorT>
CUB_RUNTIME_FUNCTION __forceinline__
cudaError_t Dispatch(
Int2Type<CUB_SEGMENTED> dispatch_to,
int timing_timing_iterations,
size_t *d_temp_storage_bytes,
cudaError_t *d_cdp_error,
void* d_temp_storage,
size_t& temp_storage_bytes,
InputIteratorT d_in,
OutputIteratorT d_out,
int num_items,
int max_segments,
OffsetIteratorT d_segment_offsets,
cub::ArgMin reduction_op,
cudaStream_t stream,
bool debug_synchronous)
{
// Invoke kernel to device reduction directly
cudaError_t error = cudaSuccess;
for (int i = 0; i < timing_timing_iterations; ++i)
{
error = DeviceSegmentedReduce::ArgMin(d_temp_storage, temp_storage_bytes,
d_in, d_out, max_segments, d_segment_offsets, d_segment_offsets + 1,
stream, debug_synchronous);
}
return error;
}
/**
* Dispatch to argmax entrypoint
*/
template <typename InputIteratorT, typename OutputIteratorT, typename OffsetIteratorT>
CUB_RUNTIME_FUNCTION __forceinline__
cudaError_t Dispatch(
Int2Type<CUB_SEGMENTED> dispatch_to,
int timing_timing_iterations,
size_t *d_temp_storage_bytes,
cudaError_t *d_cdp_error,
void* d_temp_storage,
size_t& temp_storage_bytes,
InputIteratorT d_in,
OutputIteratorT d_out,
int num_items,
int max_segments,
OffsetIteratorT d_segment_offsets,
cub::ArgMax reduction_op,
cudaStream_t stream,
bool debug_synchronous)
{
// Invoke kernel to device reduction directly
cudaError_t error = cudaSuccess;
for (int i = 0; i < timing_timing_iterations; ++i)
{
error = DeviceSegmentedReduce::ArgMax(d_temp_storage, temp_storage_bytes,
d_in, d_out, max_segments, d_segment_offsets, d_segment_offsets + 1,
stream, debug_synchronous);
}
return error;
}
//---------------------------------------------------------------------
// Dispatch to different Thrust entrypoints
//---------------------------------------------------------------------
/**
* Dispatch to reduction entrypoint (min or max specialization)
*/
template <typename InputIteratorT, typename OutputIteratorT, typename OffsetIteratorT, typename ReductionOpT>
cudaError_t Dispatch(
Int2Type<THRUST> dispatch_to,
int timing_timing_iterations,
size_t *d_temp_storage_bytes,
cudaError_t *d_cdp_error,
void* d_temp_storage,
size_t& temp_storage_bytes,
InputIteratorT d_in,
OutputIteratorT d_out,
int num_items,
int max_segments,
OffsetIteratorT d_segment_offsets,
ReductionOpT reduction_op,
cudaStream_t stream,
bool debug_synchronous)
{
// The output value type
typedef typename If<(Equals<typename std::iterator_traits<OutputIteratorT>::value_type, void>::VALUE), // OutputT = (if output iterator's value type is void) ?
typename std::iterator_traits<InputIteratorT>::value_type, // ... then the input iterator's value type,
typename std::iterator_traits<OutputIteratorT>::value_type>::Type OutputT; // ... else the output iterator's value type
if (d_temp_storage == 0)
{
temp_storage_bytes = 1;
}
else
{
OutputT init;
CubDebugExit(cudaMemcpy(&init, d_in + 0, sizeof(OutputT), cudaMemcpyDeviceToHost));
thrust::device_ptr<OutputT> d_in_wrapper(d_in);
OutputT retval;
for (int i = 0; i < timing_timing_iterations; ++i)
{
retval = thrust::reduce(d_in_wrapper, d_in_wrapper + num_items, init, reduction_op);
}
if (!Equals<OutputIteratorT, DiscardOutputIterator<int> >::VALUE)
CubDebugExit(cudaMemcpy(d_out, &retval, sizeof(OutputT), cudaMemcpyHostToDevice));
}
return cudaSuccess;
}
/**
* Dispatch to reduction entrypoint (sum specialization)
*/
template <typename InputIteratorT, typename OutputIteratorT, typename OffsetIteratorT>
cudaError_t Dispatch(
Int2Type<THRUST> dispatch_to,
int timing_timing_iterations,
size_t *d_temp_storage_bytes,
cudaError_t *d_cdp_error,
void* d_temp_storage,
size_t& temp_storage_bytes,
InputIteratorT d_in,
OutputIteratorT d_out,
int num_items,
int max_segments,
OffsetIteratorT d_segment_offsets,
Sum reduction_op,
cudaStream_t stream,
bool debug_synchronous)
{
// The output value type
typedef typename If<(Equals<typename std::iterator_traits<OutputIteratorT>::value_type, void>::VALUE), // OutputT = (if output iterator's value type is void) ?
typename std::iterator_traits<InputIteratorT>::value_type, // ... then the input iterator's value type,
typename std::iterator_traits<OutputIteratorT>::value_type>::Type OutputT; // ... else the output iterator's value type
if (d_temp_storage == 0)
{
temp_storage_bytes = 1;
}
else
{
thrust::device_ptr<OutputT> d_in_wrapper(d_in);
OutputT retval;
for (int i = 0; i < timing_timing_iterations; ++i)
{
retval = thrust::reduce(d_in_wrapper, d_in_wrapper + num_items);
}
if (!Equals<OutputIteratorT, DiscardOutputIterator<int> >::VALUE)
CubDebugExit(cudaMemcpy(d_out, &retval, sizeof(OutputT), cudaMemcpyHostToDevice));
}
return cudaSuccess;
}
//---------------------------------------------------------------------
// CUDA nested-parallelism test kernel
//---------------------------------------------------------------------
/**
* Simple wrapper kernel to invoke DeviceReduce
*/
template <
typename InputIteratorT,
typename OutputIteratorT,
typename OffsetIteratorT,
typename ReductionOpT>
__global__ void CnpDispatchKernel(
int timing_timing_iterations,
size_t *d_temp_storage_bytes,
cudaError_t *d_cdp_error,
void* d_temp_storage,
size_t temp_storage_bytes,
InputIteratorT d_in,
OutputIteratorT d_out,
int num_items,
int max_segments,
OffsetIteratorT d_segment_offsets,
ReductionOpT reduction_op,
bool debug_synchronous)
{
#ifndef CUB_CDP
*d_cdp_error = cudaErrorNotSupported;
#else
*d_cdp_error = Dispatch(Int2Type<CUB>(), timing_timing_iterations, d_temp_storage_bytes, d_cdp_error, d_temp_storage, temp_storage_bytes,
d_in, d_out, num_items, max_segments, d_segment_offsets, reduction_op, 0, debug_synchronous);
*d_temp_storage_bytes = temp_storage_bytes;
#endif
}
/**
* Dispatch to CUB_CDP kernel
*/
template <typename InputIteratorT, typename OutputIteratorT, typename OffsetIteratorT, typename ReductionOpT>
CUB_RUNTIME_FUNCTION __forceinline__
cudaError_t Dispatch(
Int2Type<CUB_CDP> dispatch_to,
int timing_timing_iterations,
size_t *d_temp_storage_bytes,
cudaError_t *d_cdp_error,
void* d_temp_storage,
size_t& temp_storage_bytes,
InputIteratorT d_in,
OutputIteratorT d_out,
int num_items,
int max_segments,
OffsetIteratorT d_segment_offsets,
ReductionOpT reduction_op,
cudaStream_t stream,
bool debug_synchronous)
{
// Invoke kernel to invoke device-side dispatch
CnpDispatchKernel<<<1,1>>>(timing_timing_iterations, d_temp_storage_bytes, d_cdp_error, d_temp_storage, temp_storage_bytes,
d_in, d_out, num_items, max_segments, d_segment_offsets, reduction_op, debug_synchronous);
// Copy out temp_storage_bytes
CubDebugExit(cudaMemcpy(&temp_storage_bytes, d_temp_storage_bytes, sizeof(size_t) * 1, cudaMemcpyDeviceToHost));
// Copy out error
cudaError_t retval;
CubDebugExit(cudaMemcpy(&retval, d_cdp_error, sizeof(cudaError_t) * 1, cudaMemcpyDeviceToHost));
return retval;
}
//---------------------------------------------------------------------
// Problem generation
//---------------------------------------------------------------------
/// Initialize problem
template <typename InputT>
void Initialize(
GenMode gen_mode,
InputT *h_in,
int num_items)
{
for (int i = 0; i < num_items; ++i)
{
InitValue(gen_mode, h_in[i], i);
}
if (g_verbose_input)
{
printf("Input:
");
DisplayResults(h_in, num_items);
printf("
");
}
}
/// Solve problem (max/custom-max functor)
template <typename ReductionOpT, typename InputT, typename _OutputT>
struct Solution
{
typedef _OutputT OutputT;
template <typename HostInputIteratorT, typename OffsetT, typename OffsetIteratorT>
static void Solve(HostInputIteratorT h_in, OutputT *h_reference, OffsetT num_segments, OffsetIteratorT h_segment_offsets,
ReductionOpT reduction_op)
{
for (int i = 0; i < num_segments; ++i)
{
OutputT aggregate = Traits<InputT>::Lowest(); // replace with std::numeric_limits<OutputT>::lowest() when C++ support is more prevalent
for (int j = h_segment_offsets[i]; j < h_segment_offsets[i + 1]; ++j)
aggregate = reduction_op(aggregate, OutputT(h_in[j]));
h_reference[i] = aggregate;
}
}
};
/// Solve problem (min functor)
template <typename InputT, typename _OutputT>
struct Solution<cub::Min, InputT, _OutputT>
{
typedef _OutputT OutputT;
template <typename HostInputIteratorT, typename OffsetT, typename OffsetIteratorT>
static void Solve(HostInputIteratorT h_in, OutputT *h_reference, OffsetT num_segments, OffsetIteratorT h_segment_offsets,
cub::Min reduction_op)
{
for (int i = 0; i < num_segments; ++i)
{
OutputT aggregate = Traits<InputT>::Max(); // replace with std::numeric_limits<OutputT>::max() when C++ support is more prevalent
for (int j = h_segment_offsets[i]; j < h_segment_offsets[i + 1]; ++j)
aggregate = reduction_op(aggregate, OutputT(h_in[j]));
h_reference[i] = aggregate;
}
}
};
/// Solve problem (sum functor)
template <typename InputT, typename _OutputT>
struct Solution<cub::Sum, InputT, _OutputT>
{
typedef _OutputT OutputT;
template <typename HostInputIteratorT, typename OffsetT, typename OffsetIteratorT>
static void Solve(HostInputIteratorT h_in, OutputT *h_reference, OffsetT num_segments, OffsetIteratorT h_segment_offsets,
cub::Sum reduction_op)
{
for (int i = 0; i < num_segments; ++i)
{
OutputT aggregate;
InitValue(INTEGER_SEED, aggregate, 0);
for (int j = h_segment_offsets[i]; j < h_segment_offsets[i + 1]; ++j)
aggregate = reduction_op(aggregate, OutputT(h_in[j]));
h_reference[i] = aggregate;
}
}
};
/// Solve problem (argmin functor)
template <typename InputValueT, typename OutputValueT>
struct Solution<cub::ArgMin, InputValueT, OutputValueT>
{
typedef KeyValuePair<int, OutputValueT> OutputT;
template <typename HostInputIteratorT, typename OffsetT, typename OffsetIteratorT>
static void Solve(HostInputIteratorT h_in, OutputT *h_reference, OffsetT num_segments, OffsetIteratorT h_segment_offsets,
cub::ArgMin reduction_op)
{
for (int i = 0; i < num_segments; ++i)
{
OutputT aggregate(1, Traits<InputValueT>::Max()); // replace with std::numeric_limits<OutputT>::max() when C++ support is more prevalent
for (int j = h_segment_offsets[i]; j < h_segment_offsets[i + 1]; ++j)
{
OutputT item(j - h_segment_offsets[i], OutputValueT(h_in[j]));
aggregate = reduction_op(aggregate, item);
}
h_reference[i] = aggregate;
}
}
};
/// Solve problem (argmax functor)
template <typename InputValueT, typename OutputValueT>
struct Solution<cub::ArgMax, InputValueT, OutputValueT>
{
typedef KeyValuePair<int, OutputValueT> OutputT;
template <typename HostInputIteratorT, typename OffsetT, typename OffsetIteratorT>
static void Solve(HostInputIteratorT h_in, OutputT *h_reference, OffsetT num_segments, OffsetIteratorT h_segment_offsets,
cub::ArgMax reduction_op)
{
for (int i = 0; i < num_segments; ++i)
{
OutputT aggregate(1, Traits<InputValueT>::Lowest()); // replace with std::numeric_limits<OutputT>::lowest() when C++ support is more prevalent
for (int j = h_segment_offsets[i]; j < h_segment_offsets[i + 1]; ++j)
{
OutputT item(j - h_segment_offsets[i], OutputValueT(h_in[j]));
aggregate = reduction_op(aggregate, item);
}
h_reference[i] = aggregate;
}
}
};
//---------------------------------------------------------------------
// Problem generation
//---------------------------------------------------------------------
/// Test DeviceReduce for a given problem input
template <
typename BackendT,
typename DeviceInputIteratorT,
typename DeviceOutputIteratorT,
typename HostReferenceIteratorT,
typename OffsetT,
typename OffsetIteratorT,
typename ReductionOpT>
void Test(
BackendT backend,
DeviceInputIteratorT d_in,
DeviceOutputIteratorT d_out,
OffsetT num_items,
OffsetT num_segments,
OffsetIteratorT d_segment_offsets,
ReductionOpT reduction_op,
HostReferenceIteratorT h_reference)
{
// Input data types
typedef typename std::iterator_traits<DeviceInputIteratorT>::value_type InputT;
// Allocate CUB_CDP device arrays for temp storage size and error
size_t *d_temp_storage_bytes = NULL;
cudaError_t *d_cdp_error = NULL;
CubDebugExit(g_allocator.DeviceAllocate((void**)&d_temp_storage_bytes, sizeof(size_t) * 1));
CubDebugExit(g_allocator.DeviceAllocate((void**)&d_cdp_error, sizeof(cudaError_t) * 1));
// Inquire temp device storage
void *d_temp_storage = NULL;
size_t temp_storage_bytes = 0;
CubDebugExit(Dispatch(backend, 1,
d_temp_storage_bytes, d_cdp_error, d_temp_storage, temp_storage_bytes,
d_in, d_out, num_items, num_segments, d_segment_offsets,
reduction_op, 0, true));
// Allocate temp device storage
CubDebugExit(g_allocator.DeviceAllocate(&d_temp_storage, temp_storage_bytes));
// Run warmup/correctness iteration
CubDebugExit(Dispatch(backend, 1,
d_temp_storage_bytes, d_cdp_error, d_temp_storage, temp_storage_bytes,
d_in, d_out, num_items, num_segments, d_segment_offsets,
reduction_op, 0, true));
// Check for correctness (and display results, if specified)
int compare = CompareDeviceResults(h_reference, d_out, num_segments, g_verbose, g_verbose);
printf("\t%s", compare ? "FAIL" : "PASS");
// Flush any stdout/stderr
fflush(stdout);
fflush(stderr);
// Performance
if (g_timing_iterations > 0)
{
GpuTimer gpu_timer;
gpu_timer.Start();
CubDebugExit(Dispatch(backend, g_timing_iterations,
d_temp_storage_bytes, d_cdp_error, d_temp_storage, temp_storage_bytes,
d_in, d_out, num_items, num_segments, d_segment_offsets,
reduction_op, 0, false));
gpu_timer.Stop();
float elapsed_millis = gpu_timer.ElapsedMillis();
// Display performance
float avg_millis = elapsed_millis / g_timing_iterations;
float giga_rate = float(num_items) / avg_millis / 1000.0f / 1000.0f;
float giga_bandwidth = giga_rate * sizeof(InputT);
printf(", %.3f avg ms, %.3f billion items/s, %.3f logical GB/s", avg_millis, giga_rate, giga_bandwidth);
}
if (d_temp_storage_bytes) CubDebugExit(g_allocator.DeviceFree(d_temp_storage_bytes));
if (d_cdp_error) CubDebugExit(g_allocator.DeviceFree(d_cdp_error));
if (d_temp_storage) CubDebugExit(g_allocator.DeviceFree(d_temp_storage));
// Correctness asserts
AssertEquals(0, compare);
}
/// Test DeviceReduce
template <
Backend BACKEND,
typename OutputValueT,
typename HostInputIteratorT,
typename DeviceInputIteratorT,
typename OffsetT,
typename OffsetIteratorT,
typename ReductionOpT>
void SolveAndTest(
HostInputIteratorT h_in,
DeviceInputIteratorT d_in,
OffsetT num_items,
OffsetT num_segments,
OffsetIteratorT h_segment_offsets,
OffsetIteratorT d_segment_offsets,
ReductionOpT reduction_op)
{
typedef typename std::iterator_traits<DeviceInputIteratorT>::value_type InputValueT;
typedef Solution<ReductionOpT, InputValueT, OutputValueT> SolutionT;
typedef typename SolutionT::OutputT OutputT;
printf("
%s cub::DeviceReduce<%s> %d items (%s), %d segments
",
(BACKEND == CUB_CDP) ? "CUB_CDP" : (BACKEND == THRUST) ? "Thrust" : (BACKEND == CUB_SEGMENTED) ? "CUB_SEGMENTED" : "CUB",
typeid(ReductionOpT).name(), num_items, typeid(HostInputIteratorT).name(), num_segments);
fflush(stdout);
// Allocate and solve solution
OutputT *h_reference = new OutputT[num_segments];
SolutionT::Solve(h_in, h_reference, num_segments, h_segment_offsets, reduction_op);
// Run with discard iterator
DiscardOutputIterator<OffsetT> discard_itr;
Test(Int2Type<BACKEND>(), d_in, discard_itr, num_items, num_segments, d_segment_offsets, reduction_op, h_reference);
// Run with output data (cleared for sanity-check)
OutputT *d_out = NULL;
CubDebugExit(g_allocator.DeviceAllocate((void**)&d_out, sizeof(OutputT) * num_segments));
CubDebugExit(cudaMemset(d_out, 0, sizeof(OutputT) * num_segments));
Test(Int2Type<BACKEND>(), d_in, d_out, num_items, num_segments, d_segment_offsets, reduction_op, h_reference);
// Cleanup
if (d_out) CubDebugExit(g_allocator.DeviceFree(d_out));
if (h_reference) delete[] h_reference;
}
/// Test specific problem type
template <
Backend BACKEND,
typename InputT,
typename OutputT,
typename OffsetT,
typename ReductionOpT>
void TestProblem(
OffsetT num_items,
OffsetT num_segments,
GenMode gen_mode,
ReductionOpT reduction_op)
{
printf("
Initializing %d %s->%s (gen mode %d)... ", num_items, typeid(InputT).name(), typeid(OutputT).name(), gen_mode); fflush(stdout);
fflush(stdout);
// Initialize value data
InputT* h_in = new InputT[num_items];
Initialize(gen_mode, h_in, num_items);
// Initialize segment data
OffsetT *h_segment_offsets = new OffsetT[num_segments + 1];
InitializeSegments(num_items, num_segments, h_segment_offsets, g_verbose_input);
// Initialize device data
OffsetT *d_segment_offsets = NULL;
InputT *d_in = NULL;
CubDebugExit(g_allocator.DeviceAllocate((void**)&d_in, sizeof(InputT) * num_items));
CubDebugExit(g_allocator.DeviceAllocate((void**)&d_segment_offsets, sizeof(OffsetT) * (num_segments + 1)));
CubDebugExit(cudaMemcpy(d_in, h_in, sizeof(InputT) * num_items, cudaMemcpyHostToDevice));
CubDebugExit(cudaMemcpy(d_segment_offsets, h_segment_offsets, sizeof(OffsetT) * (num_segments + 1), cudaMemcpyHostToDevice));
SolveAndTest<BACKEND, OutputT>(h_in, d_in, num_items, num_segments, h_segment_offsets, d_segment_offsets, reduction_op);
if (h_segment_offsets) delete[] h_segment_offsets;
if (d_segment_offsets) CubDebugExit(g_allocator.DeviceFree(d_segment_offsets));
if (h_in) delete[] h_in;
if (d_in) CubDebugExit(g_allocator.DeviceFree(d_in));
}
/// Test different operators
template <
Backend BACKEND,
typename OutputT,
typename HostInputIteratorT,
typename DeviceInputIteratorT,
typename OffsetT,
typename OffsetIteratorT>
void TestByOp(
HostInputIteratorT h_in,
DeviceInputIteratorT d_in,
OffsetT num_items,
OffsetT num_segments,
OffsetIteratorT h_segment_offsets,
OffsetIteratorT d_segment_offsets)
{
SolveAndTest<BACKEND, OutputT>(h_in, d_in, num_items, num_segments, h_segment_offsets, d_segment_offsets, CustomMax());
SolveAndTest<BACKEND, OutputT>(h_in, d_in, num_items, num_segments, h_segment_offsets, d_segment_offsets, Sum());
SolveAndTest<BACKEND, OutputT>(h_in, d_in, num_items, num_segments, h_segment_offsets, d_segment_offsets, Min());
SolveAndTest<BACKEND, OutputT>(h_in, d_in, num_items, num_segments, h_segment_offsets, d_segment_offsets, ArgMin());
SolveAndTest<BACKEND, OutputT>(h_in, d_in, num_items, num_segments, h_segment_offsets, d_segment_offsets, Max());
SolveAndTest<BACKEND, OutputT>(h_in, d_in, num_items, num_segments, h_segment_offsets, d_segment_offsets, ArgMax());
}
/// Test different backends
template <
typename InputT,
typename OutputT,
typename OffsetT>
void TestByBackend(
OffsetT num_items,
OffsetT max_segments,
GenMode gen_mode)
{
// Initialize host data
printf("
Initializing %d %s -> %s (gen mode %d)... ",
num_items, typeid(InputT).name(), typeid(OutputT).name(), gen_mode); fflush(stdout);
InputT *h_in = new InputT[num_items];
OffsetT *h_segment_offsets = new OffsetT[max_segments + 1];
Initialize(gen_mode, h_in, num_items);
// Initialize device data
InputT *d_in = NULL;
OffsetT *d_segment_offsets = NULL;
CubDebugExit(g_allocator.DeviceAllocate((void**)&d_in, sizeof(InputT) * num_items));
CubDebugExit(g_allocator.DeviceAllocate((void**)&d_segment_offsets, sizeof(OffsetT) * (max_segments + 1)));
CubDebugExit(cudaMemcpy(d_in, h_in, sizeof(InputT) * num_items, cudaMemcpyHostToDevice));
//
// Test single-segment implementations
//
InitializeSegments(num_items, 1, h_segment_offsets, g_verbose_input);
// Page-aligned-input tests
TestByOp<CUB, OutputT>(h_in, d_in, num_items, 1, h_segment_offsets, (OffsetT*) NULL); // Host-dispatch
#ifdef CUB_CDP
TestByOp<CUB_CDP, OutputT>(h_in, d_in, num_items, 1, h_segment_offsets, (OffsetT*) NULL); // Device-dispatch
#endif
// Non-page-aligned-input tests
if (num_items > 1)
{
InitializeSegments(num_items - 1, 1, h_segment_offsets, g_verbose_input);
TestByOp<CUB, OutputT>(h_in + 1, d_in + 1, num_items - 1, 1, h_segment_offsets, (OffsetT*) NULL);
}
//
// Test segmented implementation
//
// Right now we assign a single thread block to each segment, so lets keep it to under 128K items per segment
int max_items_per_segment = 128000;
for (int num_segments = (num_items + max_items_per_segment - 1) / max_items_per_segment;
num_segments < max_segments;
num_segments = (num_segments * 32) + 1)
{
// Test with segment pointer
InitializeSegments(num_items, num_segments, h_segment_offsets, g_verbose_input);
CubDebugExit(cudaMemcpy(d_segment_offsets, h_segment_offsets, sizeof(OffsetT) * (num_segments + 1), cudaMemcpyHostToDevice));
TestByOp<CUB_SEGMENTED, OutputT>(
h_in, d_in, num_items, num_segments, h_segment_offsets, d_segment_offsets);
// Test with segment iterator
typedef CastOp<OffsetT> IdentityOpT;
IdentityOpT identity_op;
TransformInputIterator<OffsetT, IdentityOpT, OffsetT*, OffsetT> h_segment_offsets_itr(
h_segment_offsets,
identity_op);
TransformInputIterator<OffsetT, IdentityOpT, OffsetT*, OffsetT> d_segment_offsets_itr(
d_segment_offsets,
identity_op);
TestByOp<CUB_SEGMENTED, OutputT>(
h_in, d_in, num_items, num_segments, h_segment_offsets_itr, d_segment_offsets_itr);
}
if (h_in) delete[] h_in;
if (h_segment_offsets) delete[] h_segment_offsets;
if (d_in) CubDebugExit(g_allocator.DeviceFree(d_in));
if (d_segment_offsets) CubDebugExit(g_allocator.DeviceFree(d_segment_offsets));
}
/// Test different input-generation modes
template <
typename InputT,
typename OutputT,
typename OffsetT>
void TestByGenMode(
OffsetT num_items,
OffsetT max_segments)
{
//
// Test pointer support using different input-generation modes
//
TestByBackend<InputT, OutputT>(num_items, max_segments, UNIFORM);
TestByBackend<InputT, OutputT>(num_items, max_segments, INTEGER_SEED);
TestByBackend<InputT, OutputT>(num_items, max_segments, RANDOM);
//
// Test iterator support using a constant-iterator and SUM
//
InputT val;
InitValue(UNIFORM, val, 0);
ConstantInputIterator<InputT, OffsetT> h_in(val);
OffsetT *h_segment_offsets = new OffsetT[1 + 1];
InitializeSegments(num_items, 1, h_segment_offsets, g_verbose_input);
SolveAndTest<CUB, OutputT>(h_in, h_in, num_items, 1, h_segment_offsets, (OffsetT*) NULL, Sum());
#ifdef CUB_CDP
SolveAndTest<CUB_CDP, OutputT>(h_in, h_in, num_items, 1, h_segment_offsets, (OffsetT*) NULL, Sum());
#endif
if (h_segment_offsets) delete[] h_segment_offsets;
}
/// Test different problem sizes
template <
typename InputT,
typename OutputT,
typename OffsetT>
struct TestBySize
{
OffsetT max_items;
OffsetT max_segments;
TestBySize(OffsetT max_items, OffsetT max_segments) :
max_items(max_items),
max_segments(max_segments)
{}
template <typename ActivePolicyT>
cudaError_t Invoke()
{
//
// Black-box testing on all backends
//
// Test 0, 1, many
TestByGenMode<InputT, OutputT>(0, max_segments);
TestByGenMode<InputT, OutputT>(1, max_segments);
TestByGenMode<InputT, OutputT>(max_items, max_segments);
// Test random problem sizes from a log-distribution [8, max_items-ish)
int num_iterations = 8;
double max_exp = log(double(max_items)) / log(double(2.0));
for (int i = 0; i < num_iterations; ++i)
{
OffsetT num_items = (OffsetT) pow(2.0, RandomValue(max_exp - 3.0) + 3.0);
TestByGenMode<InputT, OutputT>(num_items, max_segments);
}
//
// White-box testing of single-segment problems around specific sizes
//
// Tile-boundaries: multiple blocks, one tile per block
OffsetT tile_size = ActivePolicyT::ReducePolicy::BLOCK_THREADS * ActivePolicyT::ReducePolicy::ITEMS_PER_THREAD;
TestProblem<CUB, InputT, OutputT>(tile_size * 4, 1, RANDOM, Sum());
TestProblem<CUB, InputT, OutputT>(tile_size * 4 + 1, 1, RANDOM, Sum());
TestProblem<CUB, InputT, OutputT>(tile_size * 4 - 1, 1, RANDOM, Sum());
// Tile-boundaries: multiple blocks, multiple tiles per block
OffsetT sm_occupancy = 32;
OffsetT occupancy = tile_size * sm_occupancy * g_sm_count;
TestProblem<CUB, InputT, OutputT>(occupancy, 1, RANDOM, Sum());
TestProblem<CUB, InputT, OutputT>(occupancy + 1, 1, RANDOM, Sum());
TestProblem<CUB, InputT, OutputT>(occupancy - 1, 1, RANDOM, Sum());
return cudaSuccess;
}
};
/// Test problem type
template <
typename InputT,
typename OutputT,
typename OffsetT>
void TestType(
OffsetT max_items,
OffsetT max_segments)
{
typedef typename DeviceReducePolicy<OutputT, OffsetT, cub::Sum>::MaxPolicy MaxPolicyT;
TestBySize<InputT, OutputT, OffsetT> dispatch(max_items, max_segments);
MaxPolicyT::Invoke(g_ptx_version, dispatch);
}
//---------------------------------------------------------------------
// Main
//---------------------------------------------------------------------
/**
* Main
*/
int main(int argc, char** argv)
{
typedef int OffsetT;
OffsetT max_items = 27000000;
OffsetT max_segments = 34000;
// Initialize command line
CommandLineArgs args(argc, argv);
g_verbose = args.CheckCmdLineFlag("v");
g_verbose_input = args.CheckCmdLineFlag("v2");
args.GetCmdLineArgument("n", max_items);
args.GetCmdLineArgument("s", max_segments);
args.GetCmdLineArgument("i", g_timing_iterations);
args.GetCmdLineArgument("repeat", g_repeat);
// Print usage
if (args.CheckCmdLineFlag("help"))
{
printf("%s "
"[--n=<input items> "
"[--s=<num segments> "
"[--i=<timing iterations> "
"[--device=<device-id>] "
"[--repeat=<repetitions of entire test suite>]"
"[--v] "
"[--cdp]"
"
", argv[0]);
exit(0);
}
// Initialize device
CubDebugExit(args.DeviceInit());
// Get ptx version
CubDebugExit(PtxVersion(g_ptx_version));
// Get SM count
g_sm_count = args.deviceProp.multiProcessorCount;
std::numeric_limits<float>::max();
#ifdef QUICKER_TEST
// Compile/run basic test
TestProblem<CUB, int, int>( max_items, 1, RANDOM, Sum());
TestProblem<CUB, char, int>( max_items, 1, RANDOM, Sum());
TestProblem<CUB, int, int>( max_items, 1, RANDOM, ArgMax());
TestProblem<CUB, float, float>( max_items, 1, RANDOM, Sum());
TestProblem<CUB_SEGMENTED, int, int>(max_items, max_segments, RANDOM, Sum());
#elif defined(QUICK_TEST)
// Compile/run quick comparison tests
TestProblem<CUB, char, char>( max_items * 4, 1, UNIFORM, Sum());
TestProblem<THRUST, char, char>( max_items * 4, 1, UNIFORM, Sum());
printf("
----------------------------
");
TestProblem<CUB, short, short>( max_items * 2, 1, UNIFORM, Sum());
TestProblem<THRUST, short, short>( max_items * 2, 1, UNIFORM, Sum());
printf("
----------------------------
");
TestProblem<CUB, int, int>( max_items, 1, UNIFORM, Sum());
TestProblem<THRUST, int, int>( max_items, 1, UNIFORM, Sum());
printf("
----------------------------
");
TestProblem<CUB, long long, long long>( max_items / 2, 1, UNIFORM, Sum());
TestProblem<THRUST, long long, long long>( max_items / 2, 1, UNIFORM, Sum());
printf("
----------------------------
");
TestProblem<CUB, TestFoo, TestFoo>( max_items / 4, 1, UNIFORM, Max());
TestProblem<THRUST, TestFoo, TestFoo>( max_items / 4, 1, UNIFORM, Max());
#else
// Compile/run thorough tests
for (int i = 0; i <= g_repeat; ++i)
{
// Test different input types
TestType<char, char>(max_items, max_segments);
TestType<unsigned char, unsigned char>(max_items, max_segments);
TestType<char, int>(max_items, max_segments);
// TestType<short, short>(max_items, max_segments);
// TestType<int, int>(max_items, max_segments);
// TestType<long, long>(max_items, max_segments);
// TestType<long long, long long>(max_items, max_segments);
//
// TestType<uchar2, uchar2>(max_items, max_segments);
// TestType<uint2, uint2>(max_items, max_segments);
// TestType<ulonglong2, ulonglong2>(max_items, max_segments);
// TestType<ulonglong4, ulonglong4>(max_items, max_segments);
//
// TestType<TestFoo, TestFoo>(max_items, max_segments);
// TestType<TestBar, TestBar>(max_items, max_segments);
}
#endif
printf("
");
return 0;
}
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